深度学习 · generative models and autoencoders论文趋势 2023–2026
ICML/NeurIPS 深度学习方向：generative_models_and_autoencoders
统计范围：NeurIPS、ICLR、ICML、CVPR 四个会议，2023–2026 年，共 4,314 篇被归入该主题的论文。
主要结论
- 深度学习 · generative models and autoencoders论文从 2023 年的 1,216 篇增长到 2026 年的 1,430 篇，三年间扩大到约 1.2 倍。
- NeurIPS 是发深度学习 · generative models and autoencoders论文最多的会议，2023–2026 年共 1,714 篇，占四个会议总量的 40%。
- 交叉方向里涨得最快的是图像生成：同时属于两个主题的论文从 2023 年的 35 篇增至 2026 年的 150 篇。
- 在「深度学习」的 11 个主题里，它的 2026 年论文数排第 2；同组增长最快的扩散模型是 4.1 倍，深度学习 · generative models and autoencoders是 1.2 倍。
各会议论文数
按年份和会议统计
| 年份 | NeurIPS | ICLR | ICML | CVPR | 合计 |
|---|---|---|---|---|---|
| 2023 | 776 | 78 | 286 | 76 | 1,216 |
| 2024 | 349 | 174 | 139 | 106 | 768 |
| 2025 | 240 | 275 | 275 | 110 | 900 |
| 2026 | 349 | 303 | 604 | 174 | 1,430 |
代表论文
按年份和会议均匀挑选，方便快速看到每一届的样貌
- NeurIPS 2026A Critical $\beta$-Scale for Posterior Collapse in Dirichlet $\beta$-VAEs
- ICML 2026(1D) Ordered Tokens Enable Efficient Test-Time Search
- ICLR 2026``Noisier'’ Noise Contrastive Estimation is (Almost) Maximum Likelihood
- CVPR 2026Anchoring and Rescaling Attention for Semantically Coherent Inbetweening
- NeurIPS 2025Agnostic Learning under Targeted Poisoning: Optimal Rates and the Role of Randomness
- ICML 2025A Likelihood Based Approach to Distribution Regression Using Conditional Deep Generative Models
- ICLR 2025A Watermark for Order-Agnostic Language Models
- CVPR 2025DUNE: Distilling a Universal Encoder from Heterogeneous 2D and 3D Teachers
- NeurIPS 2024[Re] GNNInterpreter: A probabilistic generative model-level explanation for Graph Neural Networks
- ICML 2024A Fixed-Point Approach for Causal Generative Modeling
- ICLR 2024$t^3$-Variational Autoencoder: Learning Heavy-tailed Data with Student's t and Power Divergence
- CVPR 2024Decompose-and-Compose: A Compositional Approach to Mitigating Spurious Correlation
- NeurIPS 2023(S)GD over Diagonal Linear Networks: Implicit bias, Large Stepsizes and Edge of Stability
- ICML 2023A Kernelized Stein Discrepancy for Biological Sequences
- ICLR 2023Arbitrary Virtual Try-on Network: Characteristics Representation and Trade-off between Body and Clothing
- CVPR 20233D GAN Inversion With Facial Symmetry Prior
- NeurIPS 2026Action-On-Item Preference Flow: A Shared Event Schema for Predictive and Generative Personalization
- ICML 2026$f$-Trajectory Balance: A Loss Family for Tuning GFlowNets, Generative Models, and LLMs with Off- and On-Policy Data
- ICLR 2026Analyzing and Evaluating Unbiased Language Model Watermark
- CVPR 2026Bidirectional Normalizing Flow From Data to Noise and Back
全部深度学习 · generative models and autoencoders论文
共 349 篇，第 5 / 7 页
- NeurIPS 2026Nonparametric In-Context Learning under Growing Geometric Complexity: Minimax Optimality and Local Geometry-Adaptivity of Transformers
- NeurIPS 2026Normalizing Trajectory Models
- NeurIPS 2026Not Suppressing or Purifying: Backdoor Containment via Expert Quarantine and Shutdown in LLMs
- NeurIPS 2026Not Too Generative, Not Too Discriminative: The Human Alignment Sweet Spot
- NeurIPS 2026On approximation and estimation of Schrödinger potentials without the curse of dimensionality
- NeurIPS 2026On the Depth of Monotone ReLU Neural Networks and ICNNs
- NeurIPS 2026On the Selectivity of Generative Models in Structure-Based Drug Design
- NeurIPS 2026On the Sparsity-Storage-Accuracy Tradeoff in Parsimoniously Activated Dictionary Learning
- NeurIPS 2026On the Tightness and Computational Tractability of Higher-Dimensional Confidence Sequences
- NeurIPS 2026One-Shot Generative Flows: Existence and Obstructions
- NeurIPS 2026OneSearch-V2: The Latent Reasoning Enhanced Self-distillation Generative Search Framework
- NeurIPS 2026Optimistic Dual Averaging Unifies Modern Optimizers
- NeurIPS 2026Order-Optimal Sample Complexity for Distribution Learning via Flow Matching
- NeurIPS 2026Overcoming State Inertia in Full-Duplex Spoken Language Models via Activation Steering
- NeurIPS 2026PatchBench: Measuring Collateral Damage in Activation Patching
- NeurIPS 2026Patching Up Circuit Learning on Images
- NeurIPS 2026Path-independent Flow Matching for Multi-parameter Generative Dynamics
- NeurIPS 2026PEEK: One-Step Look-Ahead Exposure Bias Correction for Diffusion Sampling
- NeurIPS 2026Permutation-Invariant Spectral Learning via Dyson Diffusion
- NeurIPS 2026Phase Kernel Lifts Capacity of Dense Associative Memory
- NeurIPS 2026PhaseDance: Capturing Rhythm and Expressivity in Dance Modeling
- NeurIPS 2026PiD: Fast and High-Resolution Latent Decoding with Pixel Diffusion
- NeurIPS 2026Plan4D: Generative Plannable 4D Worlds
- NeurIPS 2026Plug-and-Play ADMM for Inverse Problems with Flow Matching Denoiser
- NeurIPS 2026Point-to-Manifold Geometry: Flexibly Overcoming the Curse of Dimensionality in Neural Computational Units
- NeurIPS 2026Position: We Need Greater Transparency to Maintain Research Pipeline Reliability Despite GenAI
- NeurIPS 2026Positional Encoding and Prompt-Order Sensitivity in In-Context Learning
- NeurIPS 2026PosteriorBench: From Point Estimates to Posterior Matching in Evaluating Generative Inverse Solvers
- NeurIPS 2026PP-Mark: Provable and Publicly Verifiable Watermarking for Generative AI
- NeurIPS 2026Preconditioned Flow Matching
- NeurIPS 2026Predict-Project-Renoise: Sampling Diffusion Models under Hard Constraints
- NeurIPS 2026Predicting directional flexibility in proteins
- NeurIPS 2026Prevailing Bisimulation Metric Learning Is Biased: Implicit Regularization and Its Remedy
- NeurIPS 2026PRISM:Disentangling Preference Distributions for Generative Ranking
- NeurIPS 2026Probability-Conserving Flow Guidance
- NeurIPS 2026ProDG: Prototypes for Data-Free Generative Post-Hoc Explainability
- NeurIPS 2026Projection Learning: A Principled Way to Overcome Memorization in Distribution Learning
- NeurIPS 2026R-GRec: Relation-Guided Generative Recommendation via Collaborative Graph Supervision
- NeurIPS 2026RA-CFGCache: From Branch-Level Criteria to Guided-Risk Control under Classifier-Free Guidance
- NeurIPS 2026RA-ClipScore: Making Generative Model Evaluation More Interpretable
- NeurIPS 2026RadarMAE: Injecting Physical Inductive Biases into Masked Autoencoders for Advanced Radar Object Detection
- NeurIPS 2026Rate-Constrained Edge Metadata for Sender–Receiver Generative Video Super-Resolution
- NeurIPS 2026Realism VS Accuracy: Event Sequence Forecasting from a Generative Modeling Perspective
- NeurIPS 2026Reasoning with Sampling: Cutting at Decision Points
- NeurIPS 2026Recursively Trained Diffusion Models: Limiting Collapse Distribution and Spectral Characterization
- NeurIPS 2026Reflected Schrödinger Bridge Matching
- NeurIPS 2026Reinforce Adjoint Matching: Scaling RL Post-Training of Diffusion and Flow Models
- NeurIPS 2026Relaxation-Aligned State Control for Test-Time Scaling in Generative Combinatorial Optimization
- NeurIPS 2026Renoise Consistency: Unlocking Efficient Self-Correction for Diffusion Large Language Models
- NeurIPS 2026Rethinking Knowledge Distillation for Diffusion Language Models
在 Claude、Cursor 里直接问这个趋势
接上 Paperlist MCP 后，写综述、挑方向或准备投稿时，不用离开对话框就能拿到这页的数字：每年各会议的论文数、增长倍数、涨得最快的子方向和代表论文，AI 会按你的问题整理。
可以直接复制这句话问 AI
用 paperlist 查深度学习 · generative models and autoencoders在 NeurIPS 2023–2026 的论文趋势，哪一年涨得最快？
AI 会调用的工具：get_topic_stats {"topic": "dl-generative-models-and-autoencoders"}
常见问题
- 2023–2026 年四大会议一共有多少篇深度学习 · generative models and autoencoders论文？
- 共 4,314 篇，按年份分别是 2023 年 1,216 篇、2024 年 768 篇、2025 年 900 篇、2026 年 1,430 篇。
- NeurIPS 2026 有多少篇深度学习 · generative models and autoencoders论文？
- NeurIPS 2026 有 349 篇深度学习 · generative models and autoencoders论文，上一届是 240 篇。
- ICLR 2026 有多少篇深度学习 · generative models and autoencoders论文？
- ICLR 2026 有 303 篇深度学习 · generative models and autoencoders论文，上一届是 275 篇。
- ICML 2026 有多少篇深度学习 · generative models and autoencoders论文？
- ICML 2026 有 604 篇深度学习 · generative models and autoencoders论文，上一届是 275 篇。
- CVPR 2026 有多少篇深度学习 · generative models and autoencoders论文？
- CVPR 2026 有 174 篇深度学习 · generative models and autoencoders论文，上一届是 110 篇。
- 哪个会议的深度学习 · generative models and autoencoders论文最多？
- NeurIPS，2023–2026 年共 1,714 篇。